1. Field of the Invention
The present invention relates to a method and apparatus of recognizing and searching for a face using 2nd-order independent component analysis (ICA).
2. Description of the Related Art
In the context of image processing and interpretation, a human face is an important factor in visual determination and identification. Since the early 1990's, extensive research into face recognition and facial expression interpretation has been conducted. Recently, MPEG-7 face descriptors have been proposed for face search and identification in a series of images. The face descriptors should offer rapid and accurate search of the same images as those to be extracted, compared to conventional face recognition algorithms. One of challenging problems in face recognition is how to operate on combinations of images showing great changes in illumination. Many different approaches to solving this problem have been developed.
Wang and Tan proposed a 2nd-order eigenface method for illumination-invariant face description. Kamei and Yamada extended the scope of work to use reliable features in order to describe facial symmetry and changes in illumination in different environments. For face description, Nefian and Davies used an embedded Hidden Markov Model (eHMM) approach based on discrete cosine transform (DCT), and Kim et al. developed a 2nd-order PCA mixture model (PMM).
A 2nd-order PCA method was proposed by Wang and Tan based on the observations that principal components corresponding to leading eigenvalues describe illumination changes rather than identity. First, PCA is performed on a set of training images. Images reconstructed from leading principal components corresponding to a first ten eigenvalues represent low-frequency components so the leading eigenvalues are sensitive to illumination variation. Then, the training images are obtained by subtracting the leading principal components from the reconstructed image. These images are called residual images and contain high-frequency components that are less sensitive to illumination variation. Lastly, the PCA is performed on the residual images obtained by subtracting illumination variant features.
Also, a 2nd-order PCA mixture model was introduced by Kim et al. to evaluate the probability distribution of various patterns in the facial image space. Kamei and Yamada added reliable features in order to describe facial symmetry and changes illumination in different environments.
Barlett contended that ICA produces better basis images for face description than PCA, since ICA extracts important information from the facial image space containing higher order relationships among image pixels. This was proven by experimentally, as the experimental results on FERET face datasheet show. As shown in FIG. 1, an ICA representation is superior to a PCA representation, which is due to difference in selection of basis vectors. That is, when a data distribution is not Gaussian, PCA fails to accurately describe the data while ICA is able to appropriately describe the data since PCA basis vectors are orthogonal to each other.
However, the method proposed by Barlett also has a problem in that the satisfactory effect cannot be achieved against a large change in illumination.